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Machine Learning – Quant Trading

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Overview The course will explore how to implement machine learning based on trading strategies. Learn algorithmic steps on how to …


The course will explore how to implement machine learning based on trading strategies. Learn algorithmic steps on how to gather information according to market orders. This is your first step to having a career in trading. This course is the complete package, and you will learn the ability to apply these techniques to Quant Trading. Qualifying in this course will give you a competitive edge in the industry, and make you a marketable candidate. This ’Quant Trading Using Machine Learning’ online training course takes a completely practical approach to applying Machine Learning techniques to Quant Trading. The focus is on practically applying ML techniques to develop sophisticated Quant Trading models. From setting up your own historical price database in MySQL, to writing hundreds of lines of Python code, the focus is on doing from the get-go. Supplemental Material included!

Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.

This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).

  • Who is it for?
  • Course description:
  • Course Duration:
  • Method of Assessment:
  • Certification:
  • Entry Requirement:
  • Career Path:
  • Presenter Information:
  • Quant traders who have not used Machine learning techniques before to develop trading strategies
  • Analytics professionals, modellers, big data professionals who want to get hands-on experience with Machine Learning
  • Anyone who is interested in Machine Learning and wants to learn through a practical, project-based approach

This course consists of the following modules:

  • Module 01: You, This Course & Us
  • Module 02: Developing Trading Strategies in Excel
  • Module 03: Setting up your Development Environment
  • Module 04: Setting up a Price Database
  • Module 05: Decision Trees, Ensemble Learning & Random Forests
  • Module 06: A Trading Strategy as Machine Learning Classification
  • Module 07: Feature Engineering
  • Module 08: Engineering a Complex Feature – A Categorical Variable with Past Trends
  • Module 09: Building a Machine Learning Classifier in Python
  • Module 10: Nearest Neighbors Classifier
  • Module 11: Gradient Boosted Trees
  • Module 12: Introduction to Quant Trading

From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.

At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed the course.

Successful candidates will be awarded a certificate in Machine Learning – Quant Trading.

  • Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.
  • Working knowledge of Python is necessary if you want to run the source code that is provided.
  • Basic knowledge of machine learning, especially Machine Learning classification techniques, would be helpful but it’s not mandatory.

Once you successfully complete the course in Machine Learning – Quant Trading, you will gain a recognised certification that will prove your skills in Quant Trading. You can use this qualification to have a career in this field, and enhance your opportunities further. You can demonstrate your knowledge on this subject to potential organisations, and even study more courses related to this field.

This course will provide you with the knowledge and skills to gain high level job roles in the following industries:

  • Machine learning
  • Quantitative research
  • Risk analysis
  • Quantitative trading

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

PLEASE NOTE: We do not provide any software with this course.


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Course Curriculum

Free Introduction
Machine Learning – Quant Trading FREE 00:00:00
1: You, This Course & Us
1. Introduction – You, This Course & Us!
2: Developing Trading Strategies in Excel
1. Are markets efficient or inefficient?
2. Momentum Investing
3. Mean Reversion
4. Evaluating Trading Strategies – Risk & Return
5. Evaluating Trading Strategies – The Sharpe Ratio
6. The 2 Step process – Modeling & Backtesting
7. Developing a Trading Strategy in Excel
3: Setting up your Development Environment
1. Installing Anaconda for Python
2. Installing Pycharm – a Python IDE
3. MySQL Introduced & Installed (Mac OS X)
4. MySQL Server Configuration & MySQL Workbench (Mac OS X)
5. MySQL Installation (Windows)
6. [For Linux/Mac OS Shell Newbies] Path & other Environment Variables
4: Setting up a Price Database
1. Programmatically Downloading Historical Price Data
2. Code Along – Downloading Price data from Yahoo Finance
3. Code Along – Downloading a URL in Python
4. Code Along – Downloading Price data from the NSE
5. Code Along – Unzip & process the downloaded files
6. Manually download data for 10 years
7. Code Along – Download Historical Data for 10 years
8. Inserting the Downloaded files into a Database
9. Code Along – Bulk loading downloaded files into MySQL tables
10. Data Preparation
11. Code Along – Data Preparation
12. Adjusting for Corporate Actions
13. Code Along – Adjusting for Corporate Actions 1
14. Code Along – Adjusting for Corporate Actions 2
15. Code Along – Constructing a Calendar Features table in MySQL
5: Decision Trees, Ensemble Learning & Random Forests
1. Planting the seed – What are Decision Trees?
2. Growing the Tree – Decision Tree Learning
4. Decision Tree Algorithms
5. Overfitting – The Bane of Machine Learning
6. Overfitting Continued
7. Cross-Validation
8. Regularization
9. The Wisdom Of Crowds – Ensemble Learning
10. Ensemble Learning continued – Bagging, Boosting & Stacking
11. Random Forests – Much more than trees
6: A Trading Strategy as Machine Learning Classification
1. Defining the problem – Machine Learning Classification
7: Feature Engineering
1. Know the basics – A Pandas tutorial
2. Code Along – Fetching Data from MySQL
3. Code Along – Constructing some simple features
4. Code Along – Constructing a Momentum Feature
5. Code Along – Constructing a Jump Feature
6. Code Along – Assigning Labels
7. Code Along – Putting it all together
8. Code Along – Include support features from other tickers
8: Engineering a Complex Feature – A Categorical Variable with Past Trends
1. Engineering a Categorical Variable
2. Code Along – Engineering a Categorical Variable
9: Building a Machine Learning Classifier in Python
1. Introducing Scikit-Learn
2. Introducing RandomForestClassifier
3. Training & Testing a Machine Learning Classifier
4. Compare Results from different Strategies
5. Using Class probabilities for predictions
10: Nearest Neighbors Classifier
1. A Nearest Neighbors Classifier
2. Code Along – A nearest neighbors Classifier
11: Gradient Boosted Trees
1. What are Gradient Boosted Trees?
2. Introducing XGBoost – A Python library for GBT
3. Code Along – Parameter Tuning for Gradient Boosted Classifiers
12: Introduction to Quant Trading
1. Financial Markets – Who are the players?
2. What is a Stock Market Index?
3. The Mechanics of Trading – Long Vs Short positions
4. Futures Contracts

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